1 code implementation • 7 Mar 2024 • Nikhil Mishra, Maximilian Sieb, Pieter Abbeel, Xi Chen
Deep learning methods for perception are the cornerstone of many robotic systems.
1 code implementation • 31 Jul 2023 • Nikhil Mishra, Pieter Abbeel, Xi Chen, Maximilian Sieb
Dense packing in pick-and-place systems is an important feature in many warehouse and logistics applications.
no code implementations • 3 May 2023 • Yuxuan Liu, Nikhil Mishra, Pieter Abbeel, Xi Chen
Existing state-of-the-art methods are often unable to capture meaningful uncertainty in challenging or ambiguous scenes, and as such can cause critical errors in high-performance applications.
no code implementations • 13 Oct 2022 • Yuxuan Liu, Nikhil Mishra, Maximilian Sieb, Yide Shentu, Pieter Abbeel, Xi Chen
3D bounding boxes are a widespread intermediate representation in many computer vision applications.
6 code implementations • ICML 2018 • Xi Chen, Nikhil Mishra, Mostafa Rohaninejad, Pieter Abbeel
Autoregressive generative models consistently achieve the best results in density estimation tasks involving high dimensional data, such as images or audio.
4 code implementations • ICLR 2018 • Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, Pieter Abbeel
Deep neural networks excel in regimes with large amounts of data, but tend to struggle when data is scarce or when they need to adapt quickly to changes in the task.
no code implementations • ICML 2017 • Nikhil Mishra, Pieter Abbeel, Igor Mordatch
We introduce a method for learning the dynamics of complex nonlinear systems based on deep generative models over temporal segments of states and actions.